We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

Background: The novel sodium-glucose cotransporter-2 (SGLT2) inhibitor enavogliflozin effectively lowers glycosylated hemoglobin levels and body weights without the increased risk of serious adverse events; however, the long-term clinical benefits of enavogliflozin in terms of cardiovascular and renal outcomes have not been investigated. Methods: This study is an investigator-initiated, multicenter, randomized, pragmatic, open-label, active-controlled, non-inferiority trial. Eligible participants are adults (aged ≥19 years) with type 2 diabetes mellitus (T2DM) who have a history of, or are at risk of, cardiovascular disease. A total of 2,862 participants will be randomly assigned to receive either enavogliflozin or other SGLT2 inhibitors with proven cardiorenal benefits, such as dapagliflozin or empagliflozin. The primary endpoint is the time to the first occurrence of a composite of major adverse cardiovascular or renal events (Clinical Research Information Service registration number: KCT0009243). Conclusion This trial will determine whether enavogliflozin is non-inferior to dapagliflozin or empagliflozin in terms of cardiorenal outcomes in patients with T2DM and cardiovascular risk factors. This study will elucidate the role of enavogliflozin in preventing vascular complications in patients with T2DM.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

Background: The novel sodium-glucose cotransporter-2 (SGLT2) inhibitor enavogliflozin effectively lowers glycosylated hemoglobin levels and body weights without the increased risk of serious adverse events; however, the long-term clinical benefits of enavogliflozin in terms of cardiovascular and renal outcomes have not been investigated. Methods: This study is an investigator-initiated, multicenter, randomized, pragmatic, open-label, active-controlled, non-inferiority trial. Eligible participants are adults (aged ≥19 years) with type 2 diabetes mellitus (T2DM) who have a history of, or are at risk of, cardiovascular disease. A total of 2,862 participants will be randomly assigned to receive either enavogliflozin or other SGLT2 inhibitors with proven cardiorenal benefits, such as dapagliflozin or empagliflozin. The primary endpoint is the time to the first occurrence of a composite of major adverse cardiovascular or renal events (Clinical Research Information Service registration number: KCT0009243). Conclusion This trial will determine whether enavogliflozin is non-inferior to dapagliflozin or empagliflozin in terms of cardiorenal outcomes in patients with T2DM and cardiovascular risk factors. This study will elucidate the role of enavogliflozin in preventing vascular complications in patients with T2DM.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: The novel sodium-glucose cotransporter-2 (SGLT2) inhibitor enavogliflozin effectively lowers glycosylated hemoglobin levels and body weights without the increased risk of serious adverse events; however, the long-term clinical benefits of enavogliflozin in terms of cardiovascular and renal outcomes have not been investigated. Methods: This study is an investigator-initiated, multicenter, randomized, pragmatic, open-label, active-controlled, non-inferiority trial. Eligible participants are adults (aged ≥19 years) with type 2 diabetes mellitus (T2DM) who have a history of, or are at risk of, cardiovascular disease. A total of 2,862 participants will be randomly assigned to receive either enavogliflozin or other SGLT2 inhibitors with proven cardiorenal benefits, such as dapagliflozin or empagliflozin. The primary endpoint is the time to the first occurrence of a composite of major adverse cardiovascular or renal events (Clinical Research Information Service registration number: KCT0009243). Conclusion This trial will determine whether enavogliflozin is non-inferior to dapagliflozin or empagliflozin in terms of cardiorenal outcomes in patients with T2DM and cardiovascular risk factors. This study will elucidate the role of enavogliflozin in preventing vascular complications in patients with T2DM.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: The novel sodium-glucose cotransporter-2 (SGLT2) inhibitor enavogliflozin effectively lowers glycosylated hemoglobin levels and body weights without the increased risk of serious adverse events; however, the long-term clinical benefits of enavogliflozin in terms of cardiovascular and renal outcomes have not been investigated. Methods: This study is an investigator-initiated, multicenter, randomized, pragmatic, open-label, active-controlled, non-inferiority trial. Eligible participants are adults (aged ≥19 years) with type 2 diabetes mellitus (T2DM) who have a history of, or are at risk of, cardiovascular disease. A total of 2,862 participants will be randomly assigned to receive either enavogliflozin or other SGLT2 inhibitors with proven cardiorenal benefits, such as dapagliflozin or empagliflozin. The primary endpoint is the time to the first occurrence of a composite of major adverse cardiovascular or renal events (Clinical Research Information Service registration number: KCT0009243). Conclusion This trial will determine whether enavogliflozin is non-inferior to dapagliflozin or empagliflozin in terms of cardiorenal outcomes in patients with T2DM and cardiovascular risk factors. This study will elucidate the role of enavogliflozin in preventing vascular complications in patients with T2DM.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.